Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
# data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7fdd77729588>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7fdd77694ba8>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.3.1
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function

    real_dim = (image_width, image_height, image_channels)
    
    # Real images placeholder
    inputs_real = tf.placeholder(tf.float32, (None, *real_dim))
    
    # Generator input placeholder
    z = tf.placeholder(tf.float32, (None, z_dim))
    
    # Learning rate
    learning_rate = tf.placeholder(tf.float32, shape=())
    
    return inputs_real, z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False, alpha=0.1):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function

    with tf.variable_scope('discriminator', reuse=reuse):
     
        # First convolutional layer - 14 x 14 x 64
        conv1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        conv1r = tf.maximum(alpha * conv1, conv1)
        conv1r = tf.nn.dropout(conv1r, keep_prob=0.8)
        
        # Second convolutional layer - 7 x 7 x 128
        conv2 = tf.layers.conv2d(conv1r, 128, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        conv2n = tf.layers.batch_normalization(conv2, training=True)
        conv2r = tf.maximum(alpha * conv2n, conv2n)
        conv2r = tf.nn.dropout(conv2r, keep_prob=0.8)
        
        # Third convolutional layer - 4 x 4 x 256
        conv3 = tf.layers.conv2d(conv2r, 256, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        conv3n = tf.layers.batch_normalization(conv3, training=True)
        conv3r = tf.maximum(alpha * conv3n, conv3n)
        conv3r = tf.nn.dropout(conv3r, keep_prob=0.8)
        
        # Fourth convolutional layer - 2 x 2 x 512
        conv4 = tf.layers.conv2d(conv3r, 512, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        conv4n = tf.layers.batch_normalization(conv4, training=True)
        conv4r = tf.maximum(alpha * conv4n, conv4n)
        conv4r = tf.nn.dropout(conv4r, keep_prob=0.8)
                
        # Reshape output for the final layer
        reshape = tf.reshape(conv4r,(-1, 8 * 64 * 2 * 2))
        
        # Logits
        logits = tf.layers.dense(reshape, 1)
        
        # Output
        out = tf.sigmoid(logits)
     

    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True, alpha=0.1):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    with tf.variable_scope('generator', reuse= not is_train):
        
        # Dense layer
        d = tf.layers.dense(z, 16 * 32 * 3 * 3)
        dr = tf.reshape(d, (-1, 3, 3, 16 * 32))
        drn = tf.layers.batch_normalization(dr, training=is_train)
        drnr = tf.maximum(alpha * drn, drn)
        
        # First transpose convolution - 7 x 7 x 128
        c1 = tf.layers.conv2d_transpose(drnr, 128, 3, strides=2, padding='valid', kernel_initializer=tf.contrib.layers.xavier_initializer())
        c1n = tf.layers.batch_normalization(c1, training=is_train)
        c1nr = tf.maximum(alpha * c1n, c1n)
        
        # Second transpose convolution - 14 x 14 x 64 
        c2 = tf.layers.conv2d_transpose(c1nr, 64, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        c2n = tf.layers.batch_normalization(c2, training=is_train)
        c2nr = tf.maximum(alpha * c2n, c2n)
        
        # Third transpose convolution - 28 x 28 x 32
        c3 = tf.layers.conv2d_transpose(c2nr, 32, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        c3n = tf.layers.batch_normalization(c3, training=is_train)
        c3nr = tf.maximum(alpha * c3n, c3n)
        
        # Fourth transpose convolution - 28 x 28 x out_channel_dim
        c4 = tf.layers.conv2d_transpose(c3nr, out_channel_dim, 5, strides=1, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        
        # Output
        out = tf.tanh(c4)        
    
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    
    g_model = generator(input_z, out_channel_dim, is_train=True)
    
    # Real images from discriminator
    d_model_real, d_logits_real = discriminator(input_real, reuse=False)
    
    # Fake images from discriminator
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    # Discriminator real images loss
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real) * 0.9))
    
    # Discriminator fake images loss
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    
    # Generator loss
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    # Discriminator loss
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    # Trainable variables
    t_vars = tf.trainable_variables()
    
    # Trainable discriminator variables
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    
    # Trainable generator variables
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    
    # Generator update
    gen_updates = [op for op in update_ops if op.name.startswith('generator')]
    
    # Optimizers
    with tf.control_dependencies(gen_updates):
        
        # Train optimizer for Discriminator
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        
        # Train optimizer for Generator
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    
    
   # Number of color channels
    _, image_w, image_h, n_channels = data_shape
    
    # Model input
    img, z, lr = model_inputs(image_w, image_h, n_channels, z_dim)
    
    # Losses
    d_loss, g_loss = model_loss(img, z, n_channels)
    
    # Optimizers
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            
            # Set initial steps and sums
            steps = 0
            d_loss_sum = 0
            g_loss_sum = 0
            batch_count = 0
            
            for batch_images in get_batches(batch_size):
                
                steps += 1
                batch_count += 1
                batch_images = batch_images * 2
                
                # Sample random noise for generator
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={img: batch_images, z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={z: batch_z, lr: learning_rate})

                # Update loss sums
                d_loss_sum += d_loss.eval({z: batch_z, img: batch_images})
                g_loss_sum += g_loss.eval({z: batch_z})

                # Print the losses
                if steps%20 == 0:
                    
                    # Generator output
                    show_generator_output(sess, 16, z, n_channels, data_image_mode)
                    
                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Avg. Discriminator Loss: {:.4f}...".format(d_loss_sum / batch_count),
                          "Avg. Generator Loss: {:.4f}".format(g_loss_sum / batch_count))   
                    
                    # Set loss sums back to zero
                    d_loss_sum = 0
                    g_loss_sum = 0
                    
                    # Set batch count back to zero
                    batch_count = 0
                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [ ]:
batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Avg. Discriminator Loss: 2.8858... Avg. Generator Loss: 1.8170
Epoch 1/2... Avg. Discriminator Loss: 1.3854... Avg. Generator Loss: 1.7870
Epoch 1/2... Avg. Discriminator Loss: 1.1946... Avg. Generator Loss: 1.6179
Epoch 1/2... Avg. Discriminator Loss: 1.3567... Avg. Generator Loss: 1.4536
Epoch 1/2... Avg. Discriminator Loss: 1.2434... Avg. Generator Loss: 1.6437
Epoch 1/2... Avg. Discriminator Loss: 1.3486... Avg. Generator Loss: 1.2923
Epoch 1/2... Avg. Discriminator Loss: 1.3151... Avg. Generator Loss: 1.0927
Epoch 1/2... Avg. Discriminator Loss: 1.2868... Avg. Generator Loss: 1.3015
Epoch 1/2... Avg. Discriminator Loss: 1.3815... Avg. Generator Loss: 1.3507
Epoch 1/2... Avg. Discriminator Loss: 1.1334... Avg. Generator Loss: 1.1936
Epoch 1/2... Avg. Discriminator Loss: 1.2630... Avg. Generator Loss: 1.1750
Epoch 1/2... Avg. Discriminator Loss: 1.2772... Avg. Generator Loss: 1.0924
Epoch 1/2... Avg. Discriminator Loss: 1.3499... Avg. Generator Loss: 1.0228
Epoch 1/2... Avg. Discriminator Loss: 1.2509... Avg. Generator Loss: 1.1467
Epoch 1/2... Avg. Discriminator Loss: 1.2452... Avg. Generator Loss: 1.1059
Epoch 1/2... Avg. Discriminator Loss: 1.1611... Avg. Generator Loss: 1.0592
Epoch 1/2... Avg. Discriminator Loss: 1.2245... Avg. Generator Loss: 1.1244
Epoch 1/2... Avg. Discriminator Loss: 1.2253... Avg. Generator Loss: 1.1062
Epoch 1/2... Avg. Discriminator Loss: 1.2815... Avg. Generator Loss: 1.1092
Epoch 1/2... Avg. Discriminator Loss: 1.1971... Avg. Generator Loss: 1.0583
Epoch 1/2... Avg. Discriminator Loss: 1.1873... Avg. Generator Loss: 1.1378
Epoch 1/2... Avg. Discriminator Loss: 1.1629... Avg. Generator Loss: 1.0314
Epoch 1/2... Avg. Discriminator Loss: 1.2323... Avg. Generator Loss: 1.0758
Epoch 1/2... Avg. Discriminator Loss: 1.2255... Avg. Generator Loss: 1.0896
Epoch 1/2... Avg. Discriminator Loss: 1.3099... Avg. Generator Loss: 1.1322
Epoch 1/2... Avg. Discriminator Loss: 1.1859... Avg. Generator Loss: 1.0587
Epoch 1/2... Avg. Discriminator Loss: 1.1743... Avg. Generator Loss: 1.0431
Epoch 1/2... Avg. Discriminator Loss: 1.1802... Avg. Generator Loss: 1.1182
Epoch 1/2... Avg. Discriminator Loss: 1.2311... Avg. Generator Loss: 1.0782
Epoch 1/2... Avg. Discriminator Loss: 1.2309... Avg. Generator Loss: 1.0704
Epoch 1/2... Avg. Discriminator Loss: 1.2390... Avg. Generator Loss: 1.0280
Epoch 1/2... Avg. Discriminator Loss: 1.2804... Avg. Generator Loss: 0.9875
Epoch 1/2... Avg. Discriminator Loss: 1.2126... Avg. Generator Loss: 0.9703
Epoch 1/2... Avg. Discriminator Loss: 1.2577... Avg. Generator Loss: 1.1304
Epoch 1/2... Avg. Discriminator Loss: 1.2251... Avg. Generator Loss: 0.9839
Epoch 1/2... Avg. Discriminator Loss: 1.2319... Avg. Generator Loss: 0.9915
Epoch 1/2... Avg. Discriminator Loss: 1.2454... Avg. Generator Loss: 1.0419
Epoch 1/2... Avg. Discriminator Loss: 1.2771... Avg. Generator Loss: 0.9890
Epoch 1/2... Avg. Discriminator Loss: 1.2578... Avg. Generator Loss: 0.9796
Epoch 1/2... Avg. Discriminator Loss: 1.2152... Avg. Generator Loss: 1.0143
Epoch 1/2... Avg. Discriminator Loss: 1.3043... Avg. Generator Loss: 1.0757
Epoch 1/2... Avg. Discriminator Loss: 1.2496... Avg. Generator Loss: 0.9387
Epoch 1/2... Avg. Discriminator Loss: 1.2953... Avg. Generator Loss: 1.0712
Epoch 1/2... Avg. Discriminator Loss: 1.2698... Avg. Generator Loss: 0.8804
Epoch 1/2... Avg. Discriminator Loss: 1.1599... Avg. Generator Loss: 1.0253
Epoch 1/2... Avg. Discriminator Loss: 1.2497... Avg. Generator Loss: 0.9320
Epoch 2/2... Avg. Discriminator Loss: 1.2310... Avg. Generator Loss: 0.9370
Epoch 2/2... Avg. Discriminator Loss: 1.2628... Avg. Generator Loss: 0.9329
Epoch 2/2... Avg. Discriminator Loss: 1.3508... Avg. Generator Loss: 0.9533
Epoch 2/2... Avg. Discriminator Loss: 1.2930... Avg. Generator Loss: 0.8574
Epoch 2/2... Avg. Discriminator Loss: 1.2065... Avg. Generator Loss: 0.9726
Epoch 2/2... Avg. Discriminator Loss: 1.3050... Avg. Generator Loss: 0.9277
Epoch 2/2... Avg. Discriminator Loss: 1.2906... Avg. Generator Loss: 0.9597
Epoch 2/2... Avg. Discriminator Loss: 1.3132... Avg. Generator Loss: 0.9043
Epoch 2/2... Avg. Discriminator Loss: 1.2599... Avg. Generator Loss: 0.9193
Epoch 2/2... Avg. Discriminator Loss: 1.2767... Avg. Generator Loss: 0.9477
Epoch 2/2... Avg. Discriminator Loss: 1.3209... Avg. Generator Loss: 0.9369
Epoch 2/2... Avg. Discriminator Loss: 1.2595... Avg. Generator Loss: 0.8609
Epoch 2/2... Avg. Discriminator Loss: 1.2496... Avg. Generator Loss: 0.8682
Epoch 2/2... Avg. Discriminator Loss: 1.3688... Avg. Generator Loss: 1.0633
Epoch 2/2... Avg. Discriminator Loss: 1.3894... Avg. Generator Loss: 0.8486
Epoch 2/2... Avg. Discriminator Loss: 1.3832... Avg. Generator Loss: 0.9259
Epoch 2/2... Avg. Discriminator Loss: 1.2519... Avg. Generator Loss: 0.8659
Epoch 2/2... Avg. Discriminator Loss: 1.3166... Avg. Generator Loss: 0.9332
Epoch 2/2... Avg. Discriminator Loss: 1.3414... Avg. Generator Loss: 0.9548
Epoch 2/2... Avg. Discriminator Loss: 1.2974... Avg. Generator Loss: 0.8222
Epoch 2/2... Avg. Discriminator Loss: 1.3257... Avg. Generator Loss: 0.9168
Epoch 2/2... Avg. Discriminator Loss: 1.3180... Avg. Generator Loss: 0.8559
Epoch 2/2... Avg. Discriminator Loss: 1.3579... Avg. Generator Loss: 0.8801
Epoch 2/2... Avg. Discriminator Loss: 1.3013... Avg. Generator Loss: 0.9499
Epoch 2/2... Avg. Discriminator Loss: 1.3828... Avg. Generator Loss: 0.8430
Epoch 2/2... Avg. Discriminator Loss: 1.3207... Avg. Generator Loss: 0.8536
Epoch 2/2... Avg. Discriminator Loss: 1.3634... Avg. Generator Loss: 0.9754
Epoch 2/2... Avg. Discriminator Loss: 1.4065... Avg. Generator Loss: 0.9566
Epoch 2/2... Avg. Discriminator Loss: 1.2858... Avg. Generator Loss: 0.8406
Epoch 2/2... Avg. Discriminator Loss: 1.3206... Avg. Generator Loss: 0.9089
Epoch 2/2... Avg. Discriminator Loss: 1.1627... Avg. Generator Loss: 0.9861
Epoch 2/2... Avg. Discriminator Loss: 1.3114... Avg. Generator Loss: 0.9291
Epoch 2/2... Avg. Discriminator Loss: 1.3876... Avg. Generator Loss: 0.7869
Epoch 2/2... Avg. Discriminator Loss: 1.3160... Avg. Generator Loss: 0.9116
Epoch 2/2... Avg. Discriminator Loss: 1.3364... Avg. Generator Loss: 1.0293
Epoch 2/2... Avg. Discriminator Loss: 1.2740... Avg. Generator Loss: 0.8176
Epoch 2/2... Avg. Discriminator Loss: 1.2760... Avg. Generator Loss: 0.9914
Epoch 2/2... Avg. Discriminator Loss: 1.2799... Avg. Generator Loss: 0.8187
Epoch 2/2... Avg. Discriminator Loss: 1.3631... Avg. Generator Loss: 0.8976
Epoch 2/2... Avg. Discriminator Loss: 1.5181... Avg. Generator Loss: 0.8057
Epoch 2/2... Avg. Discriminator Loss: 1.3228... Avg. Generator Loss: 0.9220
Epoch 2/2... Avg. Discriminator Loss: 1.3797... Avg. Generator Loss: 0.8350
Epoch 2/2... Avg. Discriminator Loss: 1.3024... Avg. Generator Loss: 1.0109
Epoch 2/2... Avg. Discriminator Loss: 1.3506... Avg. Generator Loss: 0.8660
Epoch 2/2... Avg. Discriminator Loss: 1.2495... Avg. Generator Loss: 0.9777
Epoch 2/2... Avg. Discriminator Loss: 1.2425... Avg. Generator Loss: 0.8820

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [ ]:
batch_size = 32
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Avg. Discriminator Loss: 2.5516... Avg. Generator Loss: 3.3581
Epoch 1/1... Avg. Discriminator Loss: 0.9620... Avg. Generator Loss: 2.0801
Epoch 1/1... Avg. Discriminator Loss: 1.2759... Avg. Generator Loss: 1.7604
Epoch 1/1... Avg. Discriminator Loss: 1.3564... Avg. Generator Loss: 1.7182
Epoch 1/1... Avg. Discriminator Loss: 1.1435... Avg. Generator Loss: 1.3152
Epoch 1/1... Avg. Discriminator Loss: 1.1652... Avg. Generator Loss: 1.7257
Epoch 1/1... Avg. Discriminator Loss: 1.0125... Avg. Generator Loss: 1.3626
Epoch 1/1... Avg. Discriminator Loss: 1.3369... Avg. Generator Loss: 1.6433
Epoch 1/1... Avg. Discriminator Loss: 1.2382... Avg. Generator Loss: 1.3871
Epoch 1/1... Avg. Discriminator Loss: 1.2777... Avg. Generator Loss: 1.2180
Epoch 1/1... Avg. Discriminator Loss: 1.3237... Avg. Generator Loss: 1.1462
Epoch 1/1... Avg. Discriminator Loss: 1.4064... Avg. Generator Loss: 0.9200
Epoch 1/1... Avg. Discriminator Loss: 1.2582... Avg. Generator Loss: 1.0649
Epoch 1/1... Avg. Discriminator Loss: 1.2407... Avg. Generator Loss: 1.0663
Epoch 1/1... Avg. Discriminator Loss: 1.3417... Avg. Generator Loss: 0.9254
Epoch 1/1... Avg. Discriminator Loss: 1.2041... Avg. Generator Loss: 1.0334
Epoch 1/1... Avg. Discriminator Loss: 1.2201... Avg. Generator Loss: 1.1141
Epoch 1/1... Avg. Discriminator Loss: 1.2813... Avg. Generator Loss: 1.0752
Epoch 1/1... Avg. Discriminator Loss: 1.2889... Avg. Generator Loss: 0.9961
Epoch 1/1... Avg. Discriminator Loss: 1.2331... Avg. Generator Loss: 1.0539
Epoch 1/1... Avg. Discriminator Loss: 1.2684... Avg. Generator Loss: 0.9838
Epoch 1/1... Avg. Discriminator Loss: 1.1300... Avg. Generator Loss: 1.1832
Epoch 1/1... Avg. Discriminator Loss: 1.2245... Avg. Generator Loss: 1.2522
Epoch 1/1... Avg. Discriminator Loss: 1.1917... Avg. Generator Loss: 1.1443
Epoch 1/1... Avg. Discriminator Loss: 1.2928... Avg. Generator Loss: 1.1835
Epoch 1/1... Avg. Discriminator Loss: 1.1590... Avg. Generator Loss: 1.1360
Epoch 1/1... Avg. Discriminator Loss: 1.2734... Avg. Generator Loss: 1.1978
Epoch 1/1... Avg. Discriminator Loss: 1.1651... Avg. Generator Loss: 1.1019
Epoch 1/1... Avg. Discriminator Loss: 1.3589... Avg. Generator Loss: 1.0252
Epoch 1/1... Avg. Discriminator Loss: 1.1671... Avg. Generator Loss: 1.1660
Epoch 1/1... Avg. Discriminator Loss: 1.0995... Avg. Generator Loss: 1.3200
Epoch 1/1... Avg. Discriminator Loss: 1.1860... Avg. Generator Loss: 1.3335
Epoch 1/1... Avg. Discriminator Loss: 1.0991... Avg. Generator Loss: 1.0612
Epoch 1/1... Avg. Discriminator Loss: 1.1190... Avg. Generator Loss: 1.2909
Epoch 1/1... Avg. Discriminator Loss: 1.1952... Avg. Generator Loss: 1.1056
Epoch 1/1... Avg. Discriminator Loss: 1.1338... Avg. Generator Loss: 1.3281
Epoch 1/1... Avg. Discriminator Loss: 1.0477... Avg. Generator Loss: 1.4548
Epoch 1/1... Avg. Discriminator Loss: 1.1622... Avg. Generator Loss: 1.7861
Epoch 1/1... Avg. Discriminator Loss: 1.1463... Avg. Generator Loss: 1.2960
Epoch 1/1... Avg. Discriminator Loss: 1.2963... Avg. Generator Loss: 1.1454
Epoch 1/1... Avg. Discriminator Loss: 1.1878... Avg. Generator Loss: 1.1147
Epoch 1/1... Avg. Discriminator Loss: 1.3539... Avg. Generator Loss: 1.1385
Epoch 1/1... Avg. Discriminator Loss: 1.2130... Avg. Generator Loss: 1.1925
Epoch 1/1... Avg. Discriminator Loss: 1.2852... Avg. Generator Loss: 1.1744
Epoch 1/1... Avg. Discriminator Loss: 1.1357... Avg. Generator Loss: 1.0472
Epoch 1/1... Avg. Discriminator Loss: 1.2837... Avg. Generator Loss: 1.3901
Epoch 1/1... Avg. Discriminator Loss: 1.1810... Avg. Generator Loss: 1.3275
Epoch 1/1... Avg. Discriminator Loss: 1.2087... Avg. Generator Loss: 1.2066
Epoch 1/1... Avg. Discriminator Loss: 1.0524... Avg. Generator Loss: 1.1968
Epoch 1/1... Avg. Discriminator Loss: 1.2389... Avg. Generator Loss: 1.0258
Epoch 1/1... Avg. Discriminator Loss: 1.2192... Avg. Generator Loss: 1.1188
Epoch 1/1... Avg. Discriminator Loss: 1.3872... Avg. Generator Loss: 0.9435
Epoch 1/1... Avg. Discriminator Loss: 1.1694... Avg. Generator Loss: 1.1358
Epoch 1/1... Avg. Discriminator Loss: 1.3909... Avg. Generator Loss: 1.0832
Epoch 1/1... Avg. Discriminator Loss: 1.2768... Avg. Generator Loss: 1.2481
Epoch 1/1... Avg. Discriminator Loss: 1.1078... Avg. Generator Loss: 1.1663
Epoch 1/1... Avg. Discriminator Loss: 1.1451... Avg. Generator Loss: 1.2695
Epoch 1/1... Avg. Discriminator Loss: 1.1832... Avg. Generator Loss: 1.1726
Epoch 1/1... Avg. Discriminator Loss: 1.2129... Avg. Generator Loss: 1.1429
Epoch 1/1... Avg. Discriminator Loss: 1.1803... Avg. Generator Loss: 1.2484
Epoch 1/1... Avg. Discriminator Loss: 1.0807... Avg. Generator Loss: 1.1349
Epoch 1/1... Avg. Discriminator Loss: 1.3183... Avg. Generator Loss: 1.3995
Epoch 1/1... Avg. Discriminator Loss: 1.0431... Avg. Generator Loss: 1.0732
Epoch 1/1... Avg. Discriminator Loss: 1.1520... Avg. Generator Loss: 1.1526
Epoch 1/1... Avg. Discriminator Loss: 1.2509... Avg. Generator Loss: 1.3203
Epoch 1/1... Avg. Discriminator Loss: 1.1331... Avg. Generator Loss: 1.4041
Epoch 1/1... Avg. Discriminator Loss: 1.1060... Avg. Generator Loss: 1.1654
Epoch 1/1... Avg. Discriminator Loss: 1.2961... Avg. Generator Loss: 1.4860
Epoch 1/1... Avg. Discriminator Loss: 1.0715... Avg. Generator Loss: 1.3800
Epoch 1/1... Avg. Discriminator Loss: 1.0507... Avg. Generator Loss: 1.3653
Epoch 1/1... Avg. Discriminator Loss: 1.1001... Avg. Generator Loss: 1.2327
Epoch 1/1... Avg. Discriminator Loss: 1.1220... Avg. Generator Loss: 1.4068
Epoch 1/1... Avg. Discriminator Loss: 1.1380... Avg. Generator Loss: 1.1821
Epoch 1/1... Avg. Discriminator Loss: 1.1031... Avg. Generator Loss: 1.4066
Epoch 1/1... Avg. Discriminator Loss: 1.0922... Avg. Generator Loss: 1.3287
Epoch 1/1... Avg. Discriminator Loss: 1.0606... Avg. Generator Loss: 1.2160
Epoch 1/1... Avg. Discriminator Loss: 1.1344... Avg. Generator Loss: 1.2763
Epoch 1/1... Avg. Discriminator Loss: 1.2149... Avg. Generator Loss: 1.2409
Epoch 1/1... Avg. Discriminator Loss: 1.1004... Avg. Generator Loss: 1.2443
Epoch 1/1... Avg. Discriminator Loss: 1.1565... Avg. Generator Loss: 1.2470
Epoch 1/1... Avg. Discriminator Loss: 1.0283... Avg. Generator Loss: 1.2161
Epoch 1/1... Avg. Discriminator Loss: 1.0011... Avg. Generator Loss: 1.4914
Epoch 1/1... Avg. Discriminator Loss: 1.1635... Avg. Generator Loss: 1.3788
Epoch 1/1... Avg. Discriminator Loss: 1.1555... Avg. Generator Loss: 0.9927
Epoch 1/1... Avg. Discriminator Loss: 1.0894... Avg. Generator Loss: 1.1640
Epoch 1/1... Avg. Discriminator Loss: 1.0544... Avg. Generator Loss: 1.4300
Epoch 1/1... Avg. Discriminator Loss: 1.0982... Avg. Generator Loss: 1.3473
Epoch 1/1... Avg. Discriminator Loss: 1.0988... Avg. Generator Loss: 1.1621
Epoch 1/1... Avg. Discriminator Loss: 1.1241... Avg. Generator Loss: 1.2534
Epoch 1/1... Avg. Discriminator Loss: 1.0989... Avg. Generator Loss: 1.4073
Epoch 1/1... Avg. Discriminator Loss: 1.1557... Avg. Generator Loss: 1.3579
Epoch 1/1... Avg. Discriminator Loss: 1.1790... Avg. Generator Loss: 1.1770
Epoch 1/1... Avg. Discriminator Loss: 1.1954... Avg. Generator Loss: 1.0856
Epoch 1/1... Avg. Discriminator Loss: 1.1931... Avg. Generator Loss: 1.2236
Epoch 1/1... Avg. Discriminator Loss: 1.2574... Avg. Generator Loss: 1.0512
Epoch 1/1... Avg. Discriminator Loss: 1.1923... Avg. Generator Loss: 1.2790
Epoch 1/1... Avg. Discriminator Loss: 1.0533... Avg. Generator Loss: 1.3043
Epoch 1/1... Avg. Discriminator Loss: 1.1599... Avg. Generator Loss: 1.2404
Epoch 1/1... Avg. Discriminator Loss: 1.1748... Avg. Generator Loss: 1.0846
Epoch 1/1... Avg. Discriminator Loss: 1.1041... Avg. Generator Loss: 1.1491
Epoch 1/1... Avg. Discriminator Loss: 1.1850... Avg. Generator Loss: 0.9988
Epoch 1/1... Avg. Discriminator Loss: 1.1565... Avg. Generator Loss: 0.9708
Epoch 1/1... Avg. Discriminator Loss: 1.1657... Avg. Generator Loss: 1.1519
Epoch 1/1... Avg. Discriminator Loss: 1.2452... Avg. Generator Loss: 1.0952
Epoch 1/1... Avg. Discriminator Loss: 1.1582... Avg. Generator Loss: 1.0931
Epoch 1/1... Avg. Discriminator Loss: 1.1292... Avg. Generator Loss: 1.1428
Epoch 1/1... Avg. Discriminator Loss: 1.1755... Avg. Generator Loss: 1.2434
Epoch 1/1... Avg. Discriminator Loss: 1.2586... Avg. Generator Loss: 1.0859
Epoch 1/1... Avg. Discriminator Loss: 1.0951... Avg. Generator Loss: 1.0907
Epoch 1/1... Avg. Discriminator Loss: 1.1741... Avg. Generator Loss: 1.1221
Epoch 1/1... Avg. Discriminator Loss: 1.1636... Avg. Generator Loss: 1.1769
Epoch 1/1... Avg. Discriminator Loss: 1.2444... Avg. Generator Loss: 1.1202
Epoch 1/1... Avg. Discriminator Loss: 1.1874... Avg. Generator Loss: 1.0935
Epoch 1/1... Avg. Discriminator Loss: 1.0998... Avg. Generator Loss: 1.1836
Epoch 1/1... Avg. Discriminator Loss: 1.1598... Avg. Generator Loss: 1.0971
Epoch 1/1... Avg. Discriminator Loss: 1.0703... Avg. Generator Loss: 1.2249
Epoch 1/1... Avg. Discriminator Loss: 1.0741... Avg. Generator Loss: 1.3131
Epoch 1/1... Avg. Discriminator Loss: 1.1830... Avg. Generator Loss: 1.2361
Epoch 1/1... Avg. Discriminator Loss: 1.1694... Avg. Generator Loss: 1.1673
Epoch 1/1... Avg. Discriminator Loss: 1.1619... Avg. Generator Loss: 1.1651
Epoch 1/1... Avg. Discriminator Loss: 1.1434... Avg. Generator Loss: 1.1567
Epoch 1/1... Avg. Discriminator Loss: 1.1587... Avg. Generator Loss: 1.0744
Epoch 1/1... Avg. Discriminator Loss: 1.1198... Avg. Generator Loss: 1.1221
Epoch 1/1... Avg. Discriminator Loss: 1.3353... Avg. Generator Loss: 1.0445
Epoch 1/1... Avg. Discriminator Loss: 1.0964... Avg. Generator Loss: 1.2586
Epoch 1/1... Avg. Discriminator Loss: 1.1724... Avg. Generator Loss: 1.0768
Epoch 1/1... Avg. Discriminator Loss: 1.1389... Avg. Generator Loss: 1.1251
Epoch 1/1... Avg. Discriminator Loss: 1.0731... Avg. Generator Loss: 1.1078
Epoch 1/1... Avg. Discriminator Loss: 1.2112... Avg. Generator Loss: 1.0764
Epoch 1/1... Avg. Discriminator Loss: 1.0780... Avg. Generator Loss: 1.3041
Epoch 1/1... Avg. Discriminator Loss: 1.1413... Avg. Generator Loss: 1.0524
Epoch 1/1... Avg. Discriminator Loss: 1.1366... Avg. Generator Loss: 1.0404
Epoch 1/1... Avg. Discriminator Loss: 1.1620... Avg. Generator Loss: 1.1512
Epoch 1/1... Avg. Discriminator Loss: 1.0523... Avg. Generator Loss: 1.1040
Epoch 1/1... Avg. Discriminator Loss: 1.2648... Avg. Generator Loss: 1.2156
Epoch 1/1... Avg. Discriminator Loss: 1.1901... Avg. Generator Loss: 0.9883
Epoch 1/1... Avg. Discriminator Loss: 1.2110... Avg. Generator Loss: 1.0049
Epoch 1/1... Avg. Discriminator Loss: 1.1657... Avg. Generator Loss: 0.9851
Epoch 1/1... Avg. Discriminator Loss: 1.1735... Avg. Generator Loss: 1.0648
Epoch 1/1... Avg. Discriminator Loss: 1.1012... Avg. Generator Loss: 1.1085
Epoch 1/1... Avg. Discriminator Loss: 1.2184... Avg. Generator Loss: 1.1887
Epoch 1/1... Avg. Discriminator Loss: 1.1626... Avg. Generator Loss: 1.0242
Epoch 1/1... Avg. Discriminator Loss: 1.2043... Avg. Generator Loss: 1.0767
Epoch 1/1... Avg. Discriminator Loss: 1.1577... Avg. Generator Loss: 1.0302
Epoch 1/1... Avg. Discriminator Loss: 1.2808... Avg. Generator Loss: 1.1795
Epoch 1/1... Avg. Discriminator Loss: 1.1711... Avg. Generator Loss: 0.9985
Epoch 1/1... Avg. Discriminator Loss: 1.2812... Avg. Generator Loss: 1.0320
Epoch 1/1... Avg. Discriminator Loss: 1.1667... Avg. Generator Loss: 1.1221
Epoch 1/1... Avg. Discriminator Loss: 1.1162... Avg. Generator Loss: 1.1898
Epoch 1/1... Avg. Discriminator Loss: 1.1385... Avg. Generator Loss: 1.1189
Epoch 1/1... Avg. Discriminator Loss: 1.1569... Avg. Generator Loss: 1.0008
Epoch 1/1... Avg. Discriminator Loss: 1.2457... Avg. Generator Loss: 1.0536
Epoch 1/1... Avg. Discriminator Loss: 1.1914... Avg. Generator Loss: 0.9982
Epoch 1/1... Avg. Discriminator Loss: 1.1873... Avg. Generator Loss: 1.1230
Epoch 1/1... Avg. Discriminator Loss: 1.1114... Avg. Generator Loss: 1.0691
Epoch 1/1... Avg. Discriminator Loss: 1.2030... Avg. Generator Loss: 1.1301
Epoch 1/1... Avg. Discriminator Loss: 1.0917... Avg. Generator Loss: 1.1928
Epoch 1/1... Avg. Discriminator Loss: 1.1555... Avg. Generator Loss: 1.1054
Epoch 1/1... Avg. Discriminator Loss: 1.1654... Avg. Generator Loss: 1.0719
Epoch 1/1... Avg. Discriminator Loss: 1.0769... Avg. Generator Loss: 1.1192
Epoch 1/1... Avg. Discriminator Loss: 1.0680... Avg. Generator Loss: 1.1057
Epoch 1/1... Avg. Discriminator Loss: 1.2384... Avg. Generator Loss: 1.0493
Epoch 1/1... Avg. Discriminator Loss: 1.1290... Avg. Generator Loss: 1.1186
Epoch 1/1... Avg. Discriminator Loss: 1.1736... Avg. Generator Loss: 1.0627
Epoch 1/1... Avg. Discriminator Loss: 1.1578... Avg. Generator Loss: 1.1327
Epoch 1/1... Avg. Discriminator Loss: 1.1372... Avg. Generator Loss: 1.0699
Epoch 1/1... Avg. Discriminator Loss: 1.1848... Avg. Generator Loss: 0.9749
Epoch 1/1... Avg. Discriminator Loss: 1.1886... Avg. Generator Loss: 0.9552
Epoch 1/1... Avg. Discriminator Loss: 1.2239... Avg. Generator Loss: 0.9368
Epoch 1/1... Avg. Discriminator Loss: 1.2331... Avg. Generator Loss: 1.0150
Epoch 1/1... Avg. Discriminator Loss: 1.1492... Avg. Generator Loss: 1.1132
Epoch 1/1... Avg. Discriminator Loss: 1.2264... Avg. Generator Loss: 0.9624
Epoch 1/1... Avg. Discriminator Loss: 1.0954... Avg. Generator Loss: 0.9916
Epoch 1/1... Avg. Discriminator Loss: 1.1927... Avg. Generator Loss: 0.8902
Epoch 1/1... Avg. Discriminator Loss: 1.1118... Avg. Generator Loss: 1.0258
Epoch 1/1... Avg. Discriminator Loss: 1.2276... Avg. Generator Loss: 0.9702
Epoch 1/1... Avg. Discriminator Loss: 1.1452... Avg. Generator Loss: 1.1088
Epoch 1/1... Avg. Discriminator Loss: 1.0985... Avg. Generator Loss: 1.0566
Epoch 1/1... Avg. Discriminator Loss: 1.2597... Avg. Generator Loss: 1.0770
Epoch 1/1... Avg. Discriminator Loss: 1.1526... Avg. Generator Loss: 1.0192
Epoch 1/1... Avg. Discriminator Loss: 1.1111... Avg. Generator Loss: 1.0054
Epoch 1/1... Avg. Discriminator Loss: 1.1938... Avg. Generator Loss: 0.9196
Epoch 1/1... Avg. Discriminator Loss: 1.3101... Avg. Generator Loss: 1.0479
Epoch 1/1... Avg. Discriminator Loss: 1.2130... Avg. Generator Loss: 0.9391
Epoch 1/1... Avg. Discriminator Loss: 1.1648... Avg. Generator Loss: 0.9782
Epoch 1/1... Avg. Discriminator Loss: 1.1880... Avg. Generator Loss: 1.0709
Epoch 1/1... Avg. Discriminator Loss: 1.1644... Avg. Generator Loss: 0.9537
Epoch 1/1... Avg. Discriminator Loss: 1.2234... Avg. Generator Loss: 0.9555
Epoch 1/1... Avg. Discriminator Loss: 1.2215... Avg. Generator Loss: 0.9237
Epoch 1/1... Avg. Discriminator Loss: 1.1929... Avg. Generator Loss: 0.9988
Epoch 1/1... Avg. Discriminator Loss: 1.1835... Avg. Generator Loss: 0.9218
Epoch 1/1... Avg. Discriminator Loss: 1.2837... Avg. Generator Loss: 0.9143
Epoch 1/1... Avg. Discriminator Loss: 1.1628... Avg. Generator Loss: 1.0628
Epoch 1/1... Avg. Discriminator Loss: 1.1560... Avg. Generator Loss: 0.9551
Epoch 1/1... Avg. Discriminator Loss: 1.2306... Avg. Generator Loss: 0.9046
Epoch 1/1... Avg. Discriminator Loss: 1.1677... Avg. Generator Loss: 0.9791
Epoch 1/1... Avg. Discriminator Loss: 1.2116... Avg. Generator Loss: 0.9159
Epoch 1/1... Avg. Discriminator Loss: 1.2487... Avg. Generator Loss: 0.9516
Epoch 1/1... Avg. Discriminator Loss: 1.1773... Avg. Generator Loss: 0.9370
Epoch 1/1... Avg. Discriminator Loss: 1.2702... Avg. Generator Loss: 0.9596
Epoch 1/1... Avg. Discriminator Loss: 1.1209... Avg. Generator Loss: 1.0515
Epoch 1/1... Avg. Discriminator Loss: 1.1637... Avg. Generator Loss: 0.9941
Epoch 1/1... Avg. Discriminator Loss: 1.2350... Avg. Generator Loss: 1.1036
Epoch 1/1... Avg. Discriminator Loss: 1.1352... Avg. Generator Loss: 1.0480
Epoch 1/1... Avg. Discriminator Loss: 1.1368... Avg. Generator Loss: 1.0355
Epoch 1/1... Avg. Discriminator Loss: 1.2387... Avg. Generator Loss: 1.1131
Epoch 1/1... Avg. Discriminator Loss: 1.2214... Avg. Generator Loss: 0.9789
Epoch 1/1... Avg. Discriminator Loss: 1.2053... Avg. Generator Loss: 1.1391
Epoch 1/1... Avg. Discriminator Loss: 1.1219... Avg. Generator Loss: 1.1411
Epoch 1/1... Avg. Discriminator Loss: 1.2849... Avg. Generator Loss: 0.9462
Epoch 1/1... Avg. Discriminator Loss: 1.1439... Avg. Generator Loss: 1.1175
Epoch 1/1... Avg. Discriminator Loss: 1.1006... Avg. Generator Loss: 1.0117
Epoch 1/1... Avg. Discriminator Loss: 1.1452... Avg. Generator Loss: 1.1855
Epoch 1/1... Avg. Discriminator Loss: 1.2016... Avg. Generator Loss: 1.0555
Epoch 1/1... Avg. Discriminator Loss: 1.0667... Avg. Generator Loss: 1.1179
Epoch 1/1... Avg. Discriminator Loss: 1.2549... Avg. Generator Loss: 0.9742
Epoch 1/1... Avg. Discriminator Loss: 1.2424... Avg. Generator Loss: 1.0202
Epoch 1/1... Avg. Discriminator Loss: 1.1175... Avg. Generator Loss: 1.1227
Epoch 1/1... Avg. Discriminator Loss: 1.2911... Avg. Generator Loss: 1.2044
Epoch 1/1... Avg. Discriminator Loss: 1.2873... Avg. Generator Loss: 0.9599
Epoch 1/1... Avg. Discriminator Loss: 1.1943... Avg. Generator Loss: 0.9292
Epoch 1/1... Avg. Discriminator Loss: 1.2320... Avg. Generator Loss: 0.8414
Epoch 1/1... Avg. Discriminator Loss: 1.1969... Avg. Generator Loss: 0.9436
Epoch 1/1... Avg. Discriminator Loss: 1.2065... Avg. Generator Loss: 0.9978
Epoch 1/1... Avg. Discriminator Loss: 1.0914... Avg. Generator Loss: 1.0335
Epoch 1/1... Avg. Discriminator Loss: 1.0731... Avg. Generator Loss: 1.1876
Epoch 1/1... Avg. Discriminator Loss: 1.1747... Avg. Generator Loss: 1.0825
Epoch 1/1... Avg. Discriminator Loss: 1.1017... Avg. Generator Loss: 1.0987
Epoch 1/1... Avg. Discriminator Loss: 1.2137... Avg. Generator Loss: 0.9388
Epoch 1/1... Avg. Discriminator Loss: 1.1170... Avg. Generator Loss: 1.1126
Epoch 1/1... Avg. Discriminator Loss: 1.2150... Avg. Generator Loss: 0.9543
Epoch 1/1... Avg. Discriminator Loss: 1.2764... Avg. Generator Loss: 0.8299
Epoch 1/1... Avg. Discriminator Loss: 1.1464... Avg. Generator Loss: 1.0034
Epoch 1/1... Avg. Discriminator Loss: 1.1530... Avg. Generator Loss: 1.0144
Epoch 1/1... Avg. Discriminator Loss: 1.2342... Avg. Generator Loss: 1.0043
Epoch 1/1... Avg. Discriminator Loss: 1.1975... Avg. Generator Loss: 0.9649
Epoch 1/1... Avg. Discriminator Loss: 1.2731... Avg. Generator Loss: 1.0426
Epoch 1/1... Avg. Discriminator Loss: 1.1412... Avg. Generator Loss: 1.0138
Epoch 1/1... Avg. Discriminator Loss: 1.1999... Avg. Generator Loss: 0.8468
Epoch 1/1... Avg. Discriminator Loss: 1.1451... Avg. Generator Loss: 1.0805
Epoch 1/1... Avg. Discriminator Loss: 1.2340... Avg. Generator Loss: 0.8942
Epoch 1/1... Avg. Discriminator Loss: 1.2575... Avg. Generator Loss: 0.9136
Epoch 1/1... Avg. Discriminator Loss: 1.2574... Avg. Generator Loss: 0.9704
Epoch 1/1... Avg. Discriminator Loss: 1.1488... Avg. Generator Loss: 0.9284
Epoch 1/1... Avg. Discriminator Loss: 1.2031... Avg. Generator Loss: 0.9977
Epoch 1/1... Avg. Discriminator Loss: 1.1580... Avg. Generator Loss: 0.9813
Epoch 1/1... Avg. Discriminator Loss: 1.1568... Avg. Generator Loss: 1.0154
Epoch 1/1... Avg. Discriminator Loss: 1.2329... Avg. Generator Loss: 0.9327
Epoch 1/1... Avg. Discriminator Loss: 1.2455... Avg. Generator Loss: 0.9519
Epoch 1/1... Avg. Discriminator Loss: 1.2334... Avg. Generator Loss: 0.8921
Epoch 1/1... Avg. Discriminator Loss: 1.2178... Avg. Generator Loss: 0.9406
Epoch 1/1... Avg. Discriminator Loss: 1.1201... Avg. Generator Loss: 1.0670
Epoch 1/1... Avg. Discriminator Loss: 1.2215... Avg. Generator Loss: 0.9102
Epoch 1/1... Avg. Discriminator Loss: 1.1321... Avg. Generator Loss: 1.0125
Epoch 1/1... Avg. Discriminator Loss: 1.2509... Avg. Generator Loss: 0.9055
Epoch 1/1... Avg. Discriminator Loss: 1.2711... Avg. Generator Loss: 0.8677
Epoch 1/1... Avg. Discriminator Loss: 1.1978... Avg. Generator Loss: 1.0005
Epoch 1/1... Avg. Discriminator Loss: 1.4025... Avg. Generator Loss: 0.7568
Epoch 1/1... Avg. Discriminator Loss: 1.1548... Avg. Generator Loss: 1.0146
Epoch 1/1... Avg. Discriminator Loss: 1.2664... Avg. Generator Loss: 0.8664
Epoch 1/1... Avg. Discriminator Loss: 1.2898... Avg. Generator Loss: 0.8728
Epoch 1/1... Avg. Discriminator Loss: 1.1861... Avg. Generator Loss: 0.9574
Epoch 1/1... Avg. Discriminator Loss: 1.2651... Avg. Generator Loss: 0.9751
Epoch 1/1... Avg. Discriminator Loss: 1.2318... Avg. Generator Loss: 0.9424
Epoch 1/1... Avg. Discriminator Loss: 1.1886... Avg. Generator Loss: 0.8381
Epoch 1/1... Avg. Discriminator Loss: 1.2067... Avg. Generator Loss: 0.9189
Epoch 1/1... Avg. Discriminator Loss: 1.2845... Avg. Generator Loss: 0.9555
Epoch 1/1... Avg. Discriminator Loss: 1.2005... Avg. Generator Loss: 1.0073
Epoch 1/1... Avg. Discriminator Loss: 1.1829... Avg. Generator Loss: 0.9747
Epoch 1/1... Avg. Discriminator Loss: 1.1926... Avg. Generator Loss: 1.0480
Epoch 1/1... Avg. Discriminator Loss: 1.1686... Avg. Generator Loss: 0.9401
Epoch 1/1... Avg. Discriminator Loss: 1.2399... Avg. Generator Loss: 0.9030
Epoch 1/1... Avg. Discriminator Loss: 1.1742... Avg. Generator Loss: 0.9518
Epoch 1/1... Avg. Discriminator Loss: 1.2999... Avg. Generator Loss: 0.9291
Epoch 1/1... Avg. Discriminator Loss: 1.1036... Avg. Generator Loss: 0.9742
Epoch 1/1... Avg. Discriminator Loss: 1.2576... Avg. Generator Loss: 0.9809
Epoch 1/1... Avg. Discriminator Loss: 1.2164... Avg. Generator Loss: 0.9371
Epoch 1/1... Avg. Discriminator Loss: 1.1710... Avg. Generator Loss: 0.9845
Epoch 1/1... Avg. Discriminator Loss: 1.1582... Avg. Generator Loss: 1.0203
Epoch 1/1... Avg. Discriminator Loss: 1.2086... Avg. Generator Loss: 0.9022
Epoch 1/1... Avg. Discriminator Loss: 1.2516... Avg. Generator Loss: 0.9088
Epoch 1/1... Avg. Discriminator Loss: 1.1884... Avg. Generator Loss: 1.0895
Epoch 1/1... Avg. Discriminator Loss: 1.1981... Avg. Generator Loss: 0.9510
Epoch 1/1... Avg. Discriminator Loss: 1.1544... Avg. Generator Loss: 1.0624
Epoch 1/1... Avg. Discriminator Loss: 1.1698... Avg. Generator Loss: 0.9461
Epoch 1/1... Avg. Discriminator Loss: 1.2775... Avg. Generator Loss: 0.8709
Epoch 1/1... Avg. Discriminator Loss: 1.1840... Avg. Generator Loss: 0.9120
Epoch 1/1... Avg. Discriminator Loss: 1.2164... Avg. Generator Loss: 0.8549
Epoch 1/1... Avg. Discriminator Loss: 1.0193... Avg. Generator Loss: 0.9615
Epoch 1/1... Avg. Discriminator Loss: 1.2112... Avg. Generator Loss: 0.8522
Epoch 1/1... Avg. Discriminator Loss: 1.1327... Avg. Generator Loss: 1.0166
Epoch 1/1... Avg. Discriminator Loss: 1.1957... Avg. Generator Loss: 0.9355
Epoch 1/1... Avg. Discriminator Loss: 1.2938... Avg. Generator Loss: 0.9509
Epoch 1/1... Avg. Discriminator Loss: 1.2117... Avg. Generator Loss: 0.8788
Epoch 1/1... Avg. Discriminator Loss: 1.2844... Avg. Generator Loss: 0.8606
Epoch 1/1... Avg. Discriminator Loss: 1.1416... Avg. Generator Loss: 1.0774
Epoch 1/1... Avg. Discriminator Loss: 1.2258... Avg. Generator Loss: 0.8979
Epoch 1/1... Avg. Discriminator Loss: 1.2331... Avg. Generator Loss: 0.8750
Epoch 1/1... Avg. Discriminator Loss: 1.1797... Avg. Generator Loss: 1.0088
Epoch 1/1... Avg. Discriminator Loss: 1.2155... Avg. Generator Loss: 0.8899
Epoch 1/1... Avg. Discriminator Loss: 1.2877... Avg. Generator Loss: 0.8883
Epoch 1/1... Avg. Discriminator Loss: 1.3097... Avg. Generator Loss: 0.9231
Epoch 1/1... Avg. Discriminator Loss: 1.2257... Avg. Generator Loss: 0.8621
Epoch 1/1... Avg. Discriminator Loss: 1.1749... Avg. Generator Loss: 0.8641
Epoch 1/1... Avg. Discriminator Loss: 1.1655... Avg. Generator Loss: 0.8494
Epoch 1/1... Avg. Discriminator Loss: 1.1949... Avg. Generator Loss: 0.9379
Epoch 1/1... Avg. Discriminator Loss: 1.1683... Avg. Generator Loss: 0.9920
Epoch 1/1... Avg. Discriminator Loss: 1.1897... Avg. Generator Loss: 0.9394

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.

In [ ]:
print("done")